Synthetic wavelengths for endpoint detection in plasma etching

ABSTRACT

Described is a method for determining an endpoint of an etch process using optical emission spectroscopy (OES) data as an input. OES data is acquired by a spectrometer in a plasma etch processing chamber. The acquired time-evolving spectral data is first filtered and de-meaned, and thereafter transformed into transformed spectral data, or trends, using multivariate analysis such as principal components analysis, in which previously calculated principal component weights are used to accomplish the transform. Grouping of the principal components weights into two separate groups corresponding to positive and negative natural wavelengths, creates separate signed trends (synthetic wavelengths).

RELATED APPLICATIONS

This application is related to the U.S. Pat. No. 9,330,990 ('990),titled “Method of endpoint detection of plasma etching process usingmultivariate analysis” and U.S. Pat. No. 10,002,804, titled “Method ofendpoint detection of plasma etching process using multivariateanalysis”.

BACKGROUND Technical Field

The present application relates to a method and system for controllingthe process of etching a structure on a substrate, for example, insemiconductor manufacturing. More particularly, it relates to a methodfor determining an endpoint of an etch process of the substrate.

Description of the Related Art

Plasma etch processes are commonly used in conjunction withphotolithography in the process of manufacturing semiconductor devices,liquid crystal displays (LCDs), light-emitting diodes (LEDs), and somephotovoltaics (PVs). Generally, a layer of a radiation-sensitivematerial, such as photoresist, is first coated on a substrate andexposed to patterned light to impart a latent image thereto. Thereafter,the exposed radiation-sensitive material is developed to remove exposedradiation-sensitive material (or unexposed, if negative tone photoresistis used), leaving a pattern of radiation-sensitive material whichexposes areas to be subsequently etched, and covers areas where noetching is desired. During the etch process, for example, a plasma etchprocess, the substrate and radiation-sensitive material pattern areexposed to energetic ions in a plasma processing chamber, so as toeffect removal of the material underlying the radiation-sensitivematerial in order to form etched features, such as vias, trenches, etc.Following etching of the features in the underlying material, theremainder of the radiation-sensitive material is removed from thesubstrate using a stripping process, to expose formed etched structuresready for further processing.

In many types of devices, such as semiconductor devices, the plasma etchprocess is performed in a first material layer overlying a secondmaterial layer, and it is important that the etch process be stoppedaccurately once the etch process has formed an opening or pattern in thefirst material layer, without continuing to etch the underlying secondmaterial layer.

For purposes of controlling the etch process, various types of endpointcontrol are utilized, some of which rely on analyzing the chemistry ofthe gas in the plasma processing chamber in order to deduce whether theetch process has progressed, for example, to an underlying layer of adifferent chemical composition than the chemical composition of thelayer being etched. Other processes may rely on direct in-situmeasurements made of structures being etched. In the former group,optical emission spectroscopy (OES) is frequently used to monitor thechemistry of the gas in the plasma processing chamber. The chemicalspecies of the gas in the plasma processing chamber are excited by theplasma excitation mechanism being used, and the excited chemical speciesproduce distinct spectral signatures in the optical emission spectrum ofthe plasma. Changes in the optical emission spectrum due to, forexample, clearing of a layer being etched, and exposing of an underlyinglayer on the substrate, can be monitored and used to precisely end theetch process, i.e., reach the endpoint, so as to avoid etching of theunderlying layer or formation of other yield defeating defects, such asundercuts, etc.

Depending on the types of structures being etched and the etch processparameters, the change of the optical emission spectrum of the plasma atthe endpoint of the etch process may be very pronounced and easy todetect, or conversely, subtle and very difficult to detect. For example,etching of structures with a very low open ratio can make endpointdetection difficult using current algorithms for processing OES data.Improvements are therefore needed to make etch endpoint detection basedon OES data more robust in such challenging etch process conditions.

SUMMARY

A feature of the present application relates to a method for determiningan etch process endpoint in an etch process, where, at the endpoint, theetch process is stopped accurately once the etch process has formed anopening or pattern in a first material layer, without continuing to etchthe underlying second material layer.

In one non-limiting embodiment, OES data is acquired for different etchprocessing runs, to obtain OES data matrices, average OES data matrices,and a mean OES data matrix. This data is used so that a multivariatemodel can be established of the acquired OES data. Once the multivariatemodel of the OES data has been established, it is used subsequently foran in-situ etch endpoint detection.

An analysis grouping wavelengths of similar behavior is used todetermine a weights vector P to transform the OES data vector into thetrend domain. Preferentially, by grouping the principal componentsweights into two separate groups corresponding to positive and negativenatural wavelengths, separate signed trends (synthetic wavelengths) arecreated.

Having determined the synthetic wavelengths, during the in-situ etchendpoint detection, functional forms of time evolving values of thesynthetic wavelengths are plotted vs. time to determine an endpoint ofthe etch process.

For example, in one embodiment, the time evolution of a ratio ofsynthetic wavelengths or the time evolution of the time derivative of aratio of synthetic wavelengths is calculated.

However, in other embodiments, any other functional forms may becalculated, such as, squares of the ratio of the synthetic wavelengthsor just a single signed synthetic wavelength or just a naturalwavelength trend.

In a further non-limiting embodiment, to compensate for OES driftbetween different wafers, the normalized OES spectrum is used in aprincipal components analysis (PCA) method.

After the time-evolving trend variable has been calculated, a decisionis made on whether an endpoint has been reached. If indeed an endpointhas been reached, the etch process ends, otherwise the etch process iscontinued, and continuously monitored for etch endpoints.

The generation of synthetic wavelengths enables similar trending as thenatural wavelengths for the endpoint detection, but with higher signalto noise ratio (SNR) endpoint signals.

BRIEF DESCRIPTION OF THE DRAWINGS

The application will be better understood in light of the descriptionwhich is given in a non-limiting manner, accompanied by the attacheddrawings in which:

FIG. 1 is a schematic of an exemplary plasma etch processing system witha light detection device including a spectrometer used for acquisitionof OES data, and a controller implementing the etch endpoint detectionmethod described herein.

FIG. 2 is an exemplary flowchart of the method of preparing etchendpoint data for later in-situ etch point detection using multivariateanalysis.

FIG. 3 is an exemplary flowchart of the method of preparing etchendpoint data for later in-situ etch point detection using PCA analysis.

FIG. 4 is an exemplary flowchart of the method of in-situ etch endpointdetection.

FIG. 5 shows an exemplary graph of time evolution of a time derivativeof a trend variable functional form involving the ratio of syntheticwavelength trends and single wavelength trends.

FIG. 6A shows an exemplary graph of time evolution of a trend variablefunctional form involving a ratio of synthetic wavelength trends.

FIG. 6B shows an exemplary graph of time evolution of the timederivative of the trend variable functional form of FIG. 5A involvingthe ratio of synthetic wavelength trends.

FIG. 7A shows an exemplary graph of time evolution of a trend variablefunctional form involving a single wavelength.

FIG. 7B shows an exemplary graph of time evolution of the timederivative of the trend variable functional form of FIG. 6A involvingthe single wavelength.

FIG. 8A shows an exemplary graph of time evolution of a time derivativeof a trend variable functional form involving a single wavelength.

FIG. 8B shows an exemplary graph of time evolution of a time derivativeof a trend variable functional form involving a ratio of syntheticwavelength trends.

FIG. 8C shows an exemplary graph of time evolution of a time derivativeof a trend variable functional form involving a single syntheticwavelength, with normalization.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Reference throughout this specification to “one embodiment” or “anembodiment” means that a particular feature, structure, material, orcharacteristic described in connection with the embodiment is includedin at least one embodiment of the application, but do not denote thatthey are present in every embodiment. Thus, the appearances of thephrases “in one embodiment” or “in an embodiment” in various placesthroughout this specification are not necessarily referring to the sameembodiment of the application. Furthermore, the particular features,structures, materials, or characteristics may be combined in anysuitable manner in one or more embodiments.

According to an embodiment of the present application, depicted in FIG.1 is a plasma etch processing system 10 and a controller 55, wherein thecontroller 55 is coupled to the plasma etch processing system 10.Controller 55 is configured to monitor the performance of the plasmaetch processing system 10 using data obtained from a variety of sensorsdisposed in the plasma etch processing system 10. For example,controller 55 can be used to control various components of the plasmaetch processing system 10, to detect faults, and to detect an endpointof an etch process.

According to the illustrated embodiment of the present applicationdepicted in FIG. 1, the plasma etch processing system 10 includes aprocess chamber 15, substrate holder 20, upon which a substrate 25 to beprocessed is affixed, gas injection system 40, and vacuum pumping system58. Substrate 25 can be, for example, a semiconductor substrate, awafer, or an LCD.

The plasma etch processing system 10 can be, for example, configured tofacilitate the generation of plasma in processing region 45 adjacent toa surface of substrate 25, where plasma is formed via collisions betweenheated electrons and an ionizable gas. An ionizable gas or mixture ofgases is introduced via gas injection system 40, and the processpressure is adjusted. Desirably, plasma is utilized to create materialsspecific to a predetermined materials process, and to aid the removal ofmaterial from the exposed surfaces of substrate 25. For example, acontroller 55 can be used to control a vacuum pumping system 58 and gasinjection system 40.

Substrate 25 can be, for example, transferred into and out of the plasmaetch processing system 10 through a slot valve (not shown) and a chamberfeed-through (not shown) via a robotic substrate transfer system whereit is received by substrate lift pins (not shown) housed withinsubstrate holder 20 and mechanically translated by devices housedtherein. Once substrate 25 is received from the substrate transfersystem, it is lowered to an upper surface of the substrate holder 20.

For example, the substrate 25 can be affixed to the substrate holder 20via an electrostatic clamping system 28. Furthermore, the substrateholder 20 can further include a cooling system including are-circulating coolant flow that receives heat from the substrate holder20 and transfers heat to a heat exchanger system (not shown), or whenheating, transfers heat from the heat exchanger system. Moreover, gascan be delivered to the back-side of the substrate via a backside gasdelivery system 26 to improve the gas-gap thermal conductance betweenthe substrate 25 and the substrate holder 20. Such a system can beutilized when temperature control of the substrate is required atelevated or reduced temperatures. For example, temperature control ofthe substrate can be useful at temperatures in excess of thesteady-state temperature achieved due to a balance of the heat fluxdelivered to the substrate 25 from the plasma and the heat flux removedfrom substrate 25 by conduction to the substrate holder 20. In otherembodiments, heating elements, such as resistive heating elements, orthermo-electric heaters/coolers can be included.

With continuing reference to FIG. 1, a process gas can be, for example,introduced to processing region 45 through gas injection system 40.Process gas can, for example, include a mixture of gases such as argon,CF₄ and O₂, or Ar, CF and O₂ for oxide etch applications, or otherchemicals, such as, for example, O₂/CO/Ar/C₄F₈, O₂/CO/Ar/CF₈,O₂/CO/Ar/C₄F₆, O₂/Ar/C₄F₆, N₂/H₂. Gas injection system 40 includes ashowerhead, where process gas is supplied from a gas delivery system(not shown) to the processing region 45 through a gas injection plenum(not shown) and a multi-orifice showerhead gas injection plate (notshown).

As further shown in FIG. 1, the plasma etch processing system 10,includes a plasma source 80. For example, RF or microwave power can becoupled from generator 82 through impedance match network or tuner 84 tothe plasma source 80. A frequency for the application of RF power to theplasma source ranges from 10 MHz to 200 MHz and is preferably 60 MHz,for capacitively-coupled (CCP), inductively-coupled (ICP), andtransformer-coupled (TCP) plasma sources. For microwave plasma sources80, such as electron cyclotron (ECR) and surface wave plasma (SWP)sources, typical frequencies of operation of generator 82 are between 1and 5 GHz, and preferably about 2.45 GHz. An example of a SWP source 80is a radial line slotted antenna (RLSA) plasma source. Moreover, thecontroller 55 can be coupled to generator 82 and impedance match networkor tuner 84 in order to control the application of RF or microwave powerto the plasma source 80.

As shown in FIG. 1, substrate holder 20 can be electrically biased at anRF voltage via the transmission of RF power from RF generator 30 throughimpedance match network 32 to substrate holder 20. The RF bias can serveto attract ions from the plasma formed in processing region 45, tofacilitate the etch process. The frequency for the application of powerto the substrate holder 20 can range from 0.1 MHz to 30 MHz and ispreferably 2 MHz. Alternately, RF power can be applied to the substrateholder 20 at multiple frequencies. Furthermore, impedance match network32 serves to maximize the transfer of RF power to plasma in processchamber 15 by minimizing the reflected power. Various match networktopologies (e.g., L-type, n-type, T-type, etc.) and automatic controlmethods can be utilized.

Various sensors are configured to receive tool data from the plasma etchprocessing system 10. The sensors can include both sensors that areintrinsic to the plasma etch processing system 10 and sensors extrinsicto the plasma etch processing system 10. Intrinsic sensors can includethose sensors pertaining to the functionality of the plasma etchprocessing system 10 such as the measurement of the Helium backside gaspressure, Helium backside flow, electrostatic chuck (ESC) voltage, ESCcurrent, substrate holder 20 temperature (or lower electrode (LEL)temperature), coolant temperature, upper electrode (UEL) temperature,forward RF power, reflected RF power, RF self-induced DC bias, RFpeak-to-peak voltage, chamber wall temperature, process gas flow rates,process gas partial pressures, chamber pressure, capacitor settings(i.e., C1 and C2 positions), a focus ring thickness, RF hours, focusring RF hours, and any statistic thereof. Alternatively, extrinsicsensors can include those not directly related to the functionality ofthe plasma etch processing system 10, such as, a light detection device34 for monitoring the light emitted from the plasma in processing region45 as shown in FIG. 1.

The light detection device 34 may include a detector, such as, a(silicon) photodiode or a photomultiplier tube (PMT) for measuring thetotal light intensity emitted from the plasma. The light detectiondevice 34 may further include an optical filter, such as, a narrow-bandinterference filter. In an alternate embodiment, the light detectiondevice 34 may include a line CCD (charge coupled device) or a CID(charge injection device) array and a light dispersing device such as agrating or a prism. Additionally, the light detection device 34 mayinclude a monochromator (e.g., grating/detector system) for measuringlight at a given wavelength, or a spectrometer (e.g., with a rotating orfixed grating) for measuring the light spectrum. The light detectiondevice 34 may include a high resolution OES sensor from Peak SensorSystems. Such an OES sensor has a broad spectrum that spans theultraviolet (UV), visible (VIS) and near infrared (NIR) light spectrums.In the Peak Sensor System, the resolution is approximately 1.4Angstroms, that is, the sensor is capable of collecting 5550 wavelengthsfrom 240 to 1000 nm. In the Peak System Sensor, the sensor is equippedwith high sensitivity miniature fiber optic UV-VIS-NIR spectrometerswhich are, in turn, integrated with 2048 pixel linear CCD arrays.

The spectrometers in one embodiment of the present application receivelight transmitted through single and bundled optical fibers, where thelight output from the optical fibers is dispersed across the line CCDarray using a fixed grating. Similar to the configuration describedabove, light transmitted through an optical vacuum window is focusedonto the input end of the optical fibers via a lens or a mirror.Different spectrometers, each specifically tuned for a given spectralrange (UV, VIS and NIR), or broadband spectrometers covering UV, VIS andNIR, form a sensor for a process chamber. Each spectrometer includes anindependent analog to digital (A/D) converter. Lastly, depending uponthe sensor utilization, a full emission spectrum can be recorded every0.01 to 1.0 seconds or faster.

Alternatively, in an embodiment, a spectrometer with all reflectiveoptics may be employed by the light detection device 34. Furthermore, inan embodiment, a single spectrometer involving a single grating and asingle detector for the entire range of light wavelengths being detectedmay be used. The design and use of optical emission spectroscopyhardware for acquiring optical OES data using e.g. the light detectiondevice 34, are well known to those skilled in the art of optical plasmadiagnostics.

Controller 55 includes a microprocessor, a memory, and a digital I/Oport (potentially including D/A and/or AD converters) capable ofgenerating control voltages sufficient to communicate and activateinputs to the plasma etch processing system 10 as well as monitoroutputs from plasma etch processing system 10. As shown in FIG. 1, thecontroller 55 may be coupled to and exchange information with the RFgenerator 30, the impedance match network 32, the gas injection system40, the vacuum pumping system 58, the backside gas delivery system 26,the electrostatic clamping system 28, and the light detection device 34.A program stored in the memory is utilized to interact with theaforementioned components of the plasma etch processing system 10according to stored process instructions. One example of controller 55is a DELL PRECISION WORKSTATION 530™, available from Dell Corporation,Austin, Tex. Controller 55 may be locally located relative to the plasmaetch processing system 10, or it may be remotely located relative to theplasma etch processing system 10. For example, the controller 55 mayexchange data with the plasma etch processing system 10 using at leastone of a direct connection, an intranet, and the internet. Controller 55may be coupled to an intranet at, for example, a customer site (i.e., adevice maker, etc.), or it may be coupled to an intranet at, forexample, a vendor site (i.e., an equipment manufacturer). Additionally,for example, the controller 55 may be coupled to the internet.Furthermore, another computer (i.e., controller, server, etc.) may, forexample, access the controller 55 to exchange data via at least one of adirect connection, an intranet, and the internet. The controller 55 alsoimplements an algorithm for detection of an endpoint of an etch processbeing performed in the plasma etch processing system 10, based on inputdata provided from the light detection device 34, as described furtherherein.

In a plasma etching process, endpoint detection (EPD) using opticalemission spectroscopy is an important technique to control the etchingconsistency among wafers. Monitoring a time varying trend created fromone or two selected optical emission wavelengths, reveals an endpoint,to pause or stop the etching process. Multivariate data analysis usingsynthetic wavelengths helps to improve the SNR and the robustness of theEPD. However, a synthetic wavelength generated from multivariate dataanalysis is generally unable to keep some intrinsic properties of thenatural wavelength, such as being physically meaningful.

Grouping of the natural wavelengths using a multivariate model (onenon-limiting example of which is PCA) may generate synthetic wavelengthswhich enable similar trending as a natural wavelength for EPD, but withhigher SNR endpoint signal. In one example, non-limiting embodiment ofthe present application, the grouping comprises selecting naturalwavelengths demonstrating either constructive or destructivecontributions and separate positive and negative weights for thewavelengths are used in the grouping of the wavelengths to transform theOES data into the PCA domain. However, other ways of grouping of thewavelengths may be used in the generation of the synthetic OES data.

The process of endpoint determination in accordance with an embodimentproceeds in two phases. In the first phase, plasma etch process runs areperformed in the plasma processing chamber 15 (step 110 in FIG. 2), andOES data is acquired using the light detection device 34 during one ormore etch processing runs performed in the plasma etch processing system10 (step 120), such that a multivariate model can be established of theacquired OES data (step 130).

Once the multivariate model of the OES data has been established, it canbe used in a second phase for in-situ etch endpoint detection, as longas the etch process being run during the second phase is reasonablysimilar in terms of structures being etched, etch process conditions,etch processing system used, etc., to those used in the one or more etchprocessing runs performed in the first phase (step 140). This is toensure the validity of multivariate model.

In one non-limiting embodiment of the endpoint determination (shown inFIG. 3) where the PCA analysis multivariate model is used with aparticular grouping of the natural wavelengths (i.e., with positive andnegative weights), the endpoint detection 200 starts with etch processruns being performed and OES data being acquired, using, for example,the light detection device 34. During each plasma etch process run,spectra is acquired n times (step 210 in FIG. 3), where n is an integergreater than 1. The sampling interval between successive OES dataacquisitions, i.e. spectra acquisitions, may vary from 0.01 to 1.0seconds or faster. Each acquired OES data set, i.e. spectrum, contains mmeasured light intensities corresponding to the m pixels of a CCDdetector, each pixel corresponding to a certain light wavelengthprojected upon the pixel by a diffraction grating which is typicallyemployed as a light dispersion device in light detection device 34. CCDdetectors may have from 256 to 8192 pixels, depending on the desiredspectral resolution, but pixel numbers of 2048 or 4096 are most commonlyused. Two dimensional detectors, for example, having 4k×4k pixels alsomay be used.

Next, OES data matrices [X]^([i]) are set up (step 215) for all plasmaetch process runs i=1, 2, . . . k. Each matrix [X]^([i]) is an n×mmatrix, where acquired spectra is arranged in rows of the matrix, suchthat the rows correspond to n instants in time when OES data is taken,and columns correspond to the pixel number m. Subsequently, an n×maverage OES data matrix [X]^(avg) is optionally calculated (step 220) byaveraging each element of all acquired matrices [X]^([i]) over all i=1,2, . . . k plasma etch process runs. Optional OES spectra normalizationmay be performed before calculating the average. When k=1, there is onlya single wafer OES measurement, in which case there is no average OESmatrix computed.

In one embodiment, the OES data matrix [X]^([i]) may be optionallynormalized as follows. The OES data matrix [X]^([i]) is an n×m matrixwith components x_(ij), where i=1, 2, . . . n and each row correspondsto an OES snapshot at time t; and j=1, 2 . . . m and each columncorresponds to a trend at wavelength λ, so each column is a singlewavelength trend. The OES data normalization may be applied in two ways.In a first way, the method selects a reference snapshot at time R (i.e.,R-throw), S_(R)=x_(R,j), and then divides every OES data by thereference snapshot, x_(i,j)=x_(i,j)/x_(R,j). There can be a single timesnapshot or the snapshot averaged in a period of time. In a second way,the method selects a reference wavelength λ_(R) (i.e., R-th column) andthen divides every wavelength by the intensity of the referencewavelength x_(i,j)=x_(i,j)/x_(R,j). Similarly, there can be a singlewavelength or an average of certain band of wavelengths. The inventorshave discovered that normalization addresses intensity drift occurringduring the OES runs between different wafers.

Subsequently, and as detailed in the '990 patent, noise is filtered(step 225) from the average OES data matrix [X]^(avg), matrices[X]^([i]) and [X]^(avg) are truncated (step 230) to remove spectraacquired during plasma startup and optionally following actual etchprocess endpoint, and a mean OES data matrix [S_(avg)] is computed (step235), wherein all elements of each column are set to the average acrossthe entire column (i.e. across all instants in time) of the elements ofthe average OES data matrix [X]^(avg), and subtracted (step 240) fromeach acquired OES data matrix [X]^([i]) i=1, 2, . . . k, to perform thestep of de-meaning, i.e. average subtraction, prior to constructing amultivariate model of the acquired OES data.

Next, in one, non-limiting embodiment, a method, PCA, of determining theprincipal components weights [P] (see step 242 between step 240 and step245 in FIG. 3) used in a multivariate analysis, to transform the OESdata, is described in the following steps. Other multivariate dataanalysis methods, for example, Independent Component Analysis (ICA)method, may be also used. PCA is an example of unsupervised trainingmethod. Other supervised methods may also be used, as long as targetvalues for each or some OES spectra are available, such as partial leastsquare (PLS), support vector machine (SVM) regression or classificationmethods. The target values might be obtained from xSEM, transmissionelectron microscopy (TEM), optical critical dimension (OCD)spectroscopy, critical dimension scanning electron microscope (CDSEM),or other tools.

During Step 1, the mean spectrum of [X] is subtracted from each row(step 240 in FIG. 3), but the data is optionally not normalized using[X]'s standard deviation.

During Step 2, a covariance matrix cov(λ)=[σ² _(kj)] is computed. Thecovariance matrix is of m×m. For each column (each wavelength), theaverage {dot over (x)}_(j) is calculated. Then, the variance of column jis:

σ² _(jj)=Σ_(i)(xi,j−{dot over (x)}j)(xi,j−{dot over (x)}j)/n−1,j=1,2 . .. m  (1)

The covariance of row k and column j is:

σ² _(kj)=Σ_(i)(xi,j−{dot over (x)}k)(xi,j−{dot over (x)}j)/n−1,σ²_(kj)=σ² _(jk) ,j=1,2 . . . m,k=1,2 . . . m,k≠j  (2)

During Step 3, the eigenvectors and the eigenvalues of the covariancematrix that satisfy the equation [Covariancematrix]·[Eigenvector]=[Eigenvalue]·[Eigenvector] are calculated. This isdone by performing singular value decomposition of covariance matrixcov(λ):

P′cov(λ)P=L  (3)

where, L is a diagonal matrix of the eigenvalues of cov(λ), and P is thematrix of the eigenvectors of cov(λ). The eigenvalues are ordered indescending order, enabling the method to find the principal componentsweights in order of significance. For example, in a particular software,the top three (maximum five) eigenvectors are used.

The de-meaned OES data [X]^([i])−[S_(avg)] is then used as input intothe multivariate analysis (step 245), such as for example, PCA, usingthe derived weights vector P derived above, to transform the OES datavector into the PCA domain.

The inventors have discovered that by grouping the principal componentsweights Pj (λj) into two separate groups corresponding to positive andnegative weighted wavelengths, separate trends Tj are created, whereT⁺j+T⁻j=Tj. Each T⁺j or T⁻j is a single positive trend. Hence, allconventional trend operations, such as snapshot normalization and takinga ratio between any of them, can be easily applied to T⁺j and T⁻j.

In one embodiment, the vector [P] is calculated, and subsequently, thepositive vector [P⁺] and the negative vector [P⁻] are formed. Forexample, [P⁺] is formed by setting all negative values in [P] to zero,and [P⁻] is formed by setting all positive values in [P] to zero, andthen taking the absolute value (i.e., converting to positive numbers).

In step 245, the de-meaned OES data [X]^([i])−[S_(avg)] along with thedetermined vector [P] are used to derive the transformed OES data intothe PCA domain

[T ⁺]=([X]−[S _(avg)])[P ⁺] and [T ⁻]=([X]−[S _(avg)])[P ⁻]  (4)

The method described herein generates synthetic wavelengths(corresponding to positive and negative weighted natural wavelengths) tocreate single signed trends (transformed OES vectors). For example,positive and negative synthetic wavelengths are created:

Λ⁺ ₁=Σ_(j=1) ^(n1) w ⁺ _(j) S _(j) and Λ⁻ ₁=Σ_(k=1) ^(n2) |w ⁻ k|S_(k)  (5)

where n1 is the number of positive weights and n2 is the number ofnegative weights, and

T ₁=Σ_(i=1) ^(n) w _(i) S _(i)=Σ_(j=1) ^(n1) w ⁺ _(j) S _(j)·Σ_(k=1)^(n2) |w ⁻ _(k) |S _(k)=Λ⁺ ₁−Λ⁻ ₁.  (6)

where, S_(i) is the intensity of λ_(i) at time t_(i), and T₁, Λ⁺ ₁ andΛ⁻ ₁ are all varying with time.

Having determined the synthetic wavelengths and the resulting trends[T⁺]=[Λ⁺] and [T⁻]=[Λ⁻], the second phase of the endpoint detectionmethod is performed by using functional forms of time evolving values of[T⁺] and [T⁻]. Since trends [T⁺] and [T⁻] are already positive signals,it is possible to divide each other to get enhanced signals withoutdoing any offset to shift the trend upward being all positive, and thenapplying the offsets to new wafers in real time. For example, in oneembodiment, the ratio T⁺ ₁ (t)/T⁺ ₃ (t) is calculated. However, in otherembodiments, any other functional forms may be calculated such assquares of the ratio of the synthetic wavelengths or just a singlesynthetic wavelength.

Since the goal of the first phase is to pre-calculate usefulmultivariate model parameters for later in-situ etch endpoint detection,various parameters are saved for later use. In step 250, the mean OESdata matrix [S_(avg)] is saved to volatile or non-volatile storagemedia, to facilitate de-meaning of in-situ measured OES data. Also inthis step, the vector [P] of principal components (PC) weights is savedto volatile or non-volatile storage media to facilitate rapidtransformation of in-situ measured OES data into a transformed OES datavector [T].

In some cases, inventors have discovered that it is useful for endpointdetection reliability to shift the calculated values of elements T_(i)of the transformed OES data vector [T], i.e. the principal components,as they evolve over time, such that they concentrate around the value ofzero, rather than grow to large positive or negative values. Thisshifting is accomplished in step 255, where at least one element T_(i)of the transformed OES data vector [T] is evaluated for each instant intime during the etch process when measurements were taken, and a minimumvalue of such element, or elements, min(T), are found. For this purpose,time-evolving data from the average OES data matrix [X_(avg)], or otherdata, may be used. This minimum value is then stored in step 260 onvolatile or non-volatile storage media for later use in in-situ endpointdetection, whereby the minimum value min (T_(i)) of an element T_(i) ofthe transformed OES data vector [T] can be used to shift thetime-evolving values of the same element T_(i) of the transformed OESdata vector [T], calculated from in-situ measured optical emissionspectroscopy (OES) data.

The stored data values on volatile or non-volatile storage media are nowready to be used in the second phase, i.e. in in-situ etch endpointdetection.

FIG. 4 shows an exemplary flowchart 300 of the process of in-situendpoint detection in the plasma etch processing system 100, equippedwith light detection device 34, having available the data saved in steps250 and 260 of flowchart 200.

In steps 310 and 315, the previously determined mean OES data matrix[S_(avg)] and the vector [P] of principal components (PC) weights areretrieved from volatile or non-volatile storage media and loaded intomemory of the controller 55 of the plasma etch processing system 10 ofFIG. 1. Controller 55 will perform all the in-situ calculations neededto determine endpoint of a plasma process. Also, if used, at least oneminimum value min(T_(i)) of an element T_(i) of the transformed OES datavector [T] can be loaded from volatile or non-volatile media into thememory of controller 55, in step 320.

In step 325, a substrate 25 is loaded into the plasma etch processingsystem 10 and a plasma is formed in processing region 45.

In step 330, the light detection device 34 is used to acquire OES datain-situ, i.e., during the etch process evolving over time.

In step 335, the retrieved mean OES data matrix [S_(avg)] elements aresubtracted from each acquired OES data set, i.e. spectrum, to de-meanthe acquired spectra prior to transformation using the already developedmultivariate model.

In step 340, the already developed PCA multivariate model is used totransform the de-meaned OES data into a transformed OES data vector [T],i.e. the principal components, using Eq. 4 and the retrieved vector [P]of principal components (PC) weights. This process is very fast becauseit involves only a simple multiplication, and is thus amenable toin-situ real-time calculation. The computed elements T_(i), e.g.,natural wavelengths Λ⁺ _(i) and Λ⁻ _(i) of the transformed OES datavector [T], as they evolve over time, can be used for the endpointdetection (step 345).

In step 350, each time-evolving element T_(i) of the transformed OESdata vector [T] can be optionally differentiated to further facilitatethe endpoint detection using trend variable slope data.

After the time-evolving trend variable has been calculated, controller55 of the plasma etch processing system 10 makes a decision (step 355)whether an endpoint has been reached. If an endpoint has been reached,the etch process ends at step 360, otherwise the etch process continuesand is continuously monitored for each endpoint via steps 330-355 offlowchart 300.

FIG. 5 shows the time evolution of the time derivative of a trendvariable for an etch process. A deep, and thus easily identifiedminimum, that the differentiated trend variable goes through at etchendpoint, is seen. The bottom group of traces corresponds to the trendobtained using the trend Λ⁺ ₁ (t)/Λ⁺ ₃ (t), in which syntheticwavelengths were applied. Various traces are shown corresponding todifferent wafers used in different etch runs. FIG. 5 also shows the timeevolution of other types of trends (for different wafers), including thetime evolution of the time derivative of a single wavelength trend atλ=656 nm, and also the time evolution of the time derivative of theratio of two single wavelength trends λ=656 nm and λ=777 nm. As seen inFIG. 5, the endpoint occurs around the 32 second point of the etchprocess.

FIG. 6A shows the time evolution of the trend Λ⁺ ₁ (t)/Λ⁻ ₃ (t) fordifferent wafers, and FIG. 6B shows the time evolution of the timederivative of the trend Λ⁺ ₁ (t)/Λ⁺ ₃ (t) for different wafers, inanother etch processing run.

FIG. 7A shows the time evolution of a single wavelength trend at λ=656nm for different wafers, and FIG. 7B shows the time evolution of thetime derivative of the single wavelength at λ=656 nm for differentwafers, in another etch processing run.

FIG. 8A shows the time evolution of the time derivative of a singlewavelength trend at λ=260 nm for different wafers, and FIG. 8B shows thetime evolution of the time derivative of the trend obtained using thetrend Λ⁺ ₁ (t)/Λ⁺ ₃ (t) for different wafers, in another etch processingrun.

FIG. 8C shows the time evolution of the time derivative of a syntheticwavelength trend using the normalization discussed above. The plotrefers to a just a synthetic wavelength, not a ratio, sincenormalization has been pre-applied.

After the time-evolving trend variable has been calculated, controller55 of the plasma etch processing system 10 needs to make a decision, instep 355, whether an endpoint has been reached. If indeed an endpointhas been reached, the etch process is ended at step 360, otherwise theetch process is continued, and continuously monitored for etch endpointvia steps 330-355 of flowchart 300.

Numerous modifications and variations of the present application arepossible in light of the above teachings. It is therefore to beunderstood that within the scope of the appended claims, the applicationmay be practiced otherwise than as specifically described herein.

1. A method for determining etch process endpoint data in a plasmaprocessing system, the method comprising: performing plasma etch processruns in a plasma processing chamber of an etch processing system;acquiring optical emission spectroscopy (OES) data from the plasmaprocessing chamber during one or more etching processes; performing amultivariate data analysis on the OES data to generate synthetic OESdata from the OES data by grouping emitted wavelengths; and using thesynthetic OES data for later use in an in-situ determination of the etchprocess endpoint.
 2. The method of claim 1, where the generatingsynthetic OES data comprises obtaining a transformed OES data vector[T], wherein[T]=([X]−[Savg])[P], where [X] is an OES data matrix, [P] is a weightsvector, and [Savg] is an n×m mean OES data matrix, whose each element iscomputed as an average value of n elements of the corresponding columnof [X]avg, which is an n×m average OES data matrix, whose each elementis computed as an average of corresponding elements of the OES datamatrix [X] over k etch process runs, n corresponding to instants in timewhen OES data is taken and m corresponding to the number of measuredlight intensities in the plasma processing chamber by a detector.
 3. Themethod of claim 2, wherein the generating synthetic OES data comprisesgrouping wavelengths corresponding to positive and negative weights. 4.The method of claim 3, wherein the weights vector [P] is determined by:calculating eigenvectors and eigenvalues of a covariance matrixassociated with matrix [X]; ordering the eigenvalues in descendingorder, the eigenvalues representing the weights vector [P]; and settinga positive weights vector [P+] by setting all negative components in [P]to zero and setting a negative weights vector [P−] by setting allpositive components in [P] to zero and taking the absolute valuethereof.
 5. The method according to claim 4, further comprising:obtaining a transformed OES data vector [T+] or [T−], wherein[T+]=([X]−[Savg])[P+],[T−]=([X]−[Savg])[P−].
 6. The method according toclaim 5, further comprising: selecting a functional form involvingelements of the transformed OES data vector [T+], or [T−], and computinga time evolution of the selected functional form.
 7. The methodaccording to claim 6, further comprising: computing a time derivative ofthe selected functional form and computing a time evolution of the timederivative of the selected functional form.
 8. The method according toclaim 7, wherein the functional form comprises [T+], [T−], the ratio[T+]/[T−], a power of the ratio [T+]/[T−], or a single element of thetransformed OES data vector [T+] or [T−] or any mathematical form using[T+] and/or [T−].
 9. The method of claim 1, further comprisingperforming k plasma etch process runs in the plasma processing chamber,where k is an integer greater than zero, each of the k plasma etchprocess runs comprising: loading a substrate to be processed to theplasma processing chamber, the plasma processing chamber comprising aspectrometer having a detector comprising m pixels, each pixelcorresponding to a different light wavelength; forming a plasma in theplasma etch processing chamber; and acquiring OES data from the plasmaprocessing chamber during one or more etching processes, and forming anOES data matrix [X] for each of the k plasma etch process runs.
 10. Themethod of claim 9, further comprising computing an n×m average OES datamatrix [X]avg, wherein each element is computed as an average ofcorresponding elements of the OES matrix [X] over the k etch processruns; filtering noise from the average OES data matrix [X]avg;truncating each OES data matrix [X] and [X]avg, where data acquiredduring plasma startup and for times beyond an etch process endpoint isdiscarded; calculating an n×m mean OES data matrix [Savg], where eachelement is computed as an average value of n elements of thecorresponding column of [X]avg; subtracting [Savg] from matrix [X], foreach k, to de-mean the OES data.
 11. The method according to claim 2,wherein after forming the OES data matrix [X] for each of the plasmaetch process runs, and before computing the n×m average OES data matrix[X]avg, the method normalizes the OES data matrix [X].
 12. The methodaccording to claim 11, wherein the OES data matrix normalizationcomprises selecting a reference snapshot at time R, xR,j, and thendividing every OES data by the reference snapshot, xi,j=xi,j/xR,j. 13.The method according to claim 12, wherein the reference snapshot is asingle time snapshot or a snapshot averaged over a period of time. 14.The method according to claim 11, wherein the OES data matrixnormalization comprises selecting a reference wavelength R, and thendividing every OES data by the intensity at the reference wavelengthxi,j=xi,j/xi,R.
 15. The method according to claim 14, wherein thereference wavelength is a single wavelength or an average of bandwavelengths.
 16. A method for determining etch process endpoint data ina plasma processing system, the method comprising: performing plasmaetch process runs in a plasma processing chamber of an etch processingsystem; acquiring optical emission spectroscopy (OES) data from theplasma processing chamber during one or more etching processes;performing a multivariate data analysis on the OES data to generatesynthetic OES data from the OES data by grouping emitted wavelengthscorresponding to positive and negative weights associated with naturalwavelengths; and using the synthetic OES data for later use in anin-situ determination of the etch process endpoint.
 17. The method ofclaim 16, wherein the multivariate data analysis is performed usingindependent component analysis.
 18. The method of claim 16, wherein themultivariate data analysis is performed using a supervised multivariatedata analysis method, the supervised multivariate data analysis methodincluding support vector machine regression.
 19. The method of claim 16,further comprising: obtaining a transformed OES data vector [T+] or[T−], wherein[T+]=([X]−[Savg])[P+],[T−]=([X]−[Savg])[P−], wherein [X] is an OES datamatrix, [P+] is a positive weights vector, [P−] is a negative weightsvector, and [Savg] is an n×m mean OES data matrix, whose each element iscomputed as an average value of n elements of the corresponding columnof [X]avg, which is an n×m average OES data matrix, whose each elementis computed as an average of corresponding elements of the OES datamatrix [X] over k etch process runs, n corresponding to instants in timewhen OES data is taken and m corresponding to the number of measuredlight intensities in the plasma processing chamber by a detector. 20.The method according to claim 19, further comprising: selecting afunctional form involving elements of the transformed OES data vector[T+], or [T−], and computing a time evolution of the selected functionalform.
 21. The method according to claim 20, further comprising:computing a time derivative of the selected functional form andcomputing a time evolution of the time derivative of the selectedfunctional form.
 22. The method according to claim 21, wherein thefunctional form comprises [T+], [T−], the ratio [T+]/[T−], a power ofthe ratio [T+]/[T−], or a single element of the transformed OES datavector [T+] or [T−] or any mathematical form using [T+] and/or [T−].